Generating a correlation search

ABSTRACT

Systems and methods for assigning scores to objects based on evaluating triggering conditions applied to datasets produced by search queries in data aggregation and analysis systems. An example method includes causing display of a user interface for generating a correlation search, the correlation search comprising a search query, a triggering condition to be applied to a dataset produced by the search query, and one or more actions to be performed when the dataset produced by the search query satisfies the triggering condition, wherein the one or more actions comprise at least modifying a score assigned to an object to which the dataset produced by the search query pertains. The example method also includes receiving, via the user interface, user input identifying the one or more actions to be performed when the dataset produced by the search query satisfies the triggering condition, the one or more actions comprising modifying the score assigned to the object, and causing generation of the correlation search based on the user input, the correlation search reflecting an association between the one or more actions and the triggering condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 16/715,015, filed Dec. 16, 2019, which is acontinuation of U.S. Non-Provisional patent application Ser. No.14/977,432, filed Dec. 21, 2015, now U.S. Pat. No. 11,100,113 which is acontinuation of U.S. Non-Provisional patent application Ser. No.14/447,995, filed Jul. 31, 2014, now U.S. Pat. No. 9,251,221, whichclaims the benefit of priority from U.S. Provisional Application No.62/027,239, filed on Jul. 21, 2014, each of which is incorporated hereinby reference.

TECHNICAL FIELD

The disclosure is generally related to data aggregation and analysissystems, and is more specifically related to assigning scores to objectsbased on evaluating triggering conditions applied to datasets producedby search queries.

BACKGROUND

Modern data centers often comprise thousands of hosts that operatecollectively to service requests from even larger numbers of remoteclients. During operation, components of these data centers can producesignificant volumes of machine-generated data. The unstructured natureof much of this data has made it challenging to perform indexing andsearching operations because of the difficulty of applying semanticmeaning to unstructured data. As the number of hosts and clientsassociated with a data center continues to grow, processing largevolumes of machine-generated data in an intelligent manner andeffectively presenting the results of such processing continues to be apriority.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is illustrated by way of examples, and not by way oflimitation, and may be more fully understood with references to thefollowing detailed description when considered in connection with thefigures, in which:

FIG. 1 schematically illustrates an example GUI for specifying securityscore modification rules, including search queries, triggeringconditions, and other information to be utilized by the system forassigning and/or modifying security risk scores associated with variousobjects, in accordance with one or more aspects of the presentdisclosure;

FIG. 2 schematically illustrates an example GUI for visually presentingsecurity risk scores assigned to a plurality of objects, in accordancewith one or more aspects of the present disclosure;

FIGS. 3A-3B depict flow diagrams of example methods 300A-300B forassigning scores to objects based on evaluating triggering conditionsapplied to datasets produced by search queries, in accordance with oneor more aspects of the present disclosure;

FIG. 4 presents a block diagram of an event-processing system inaccordance with one or more aspects of the present disclosure;

FIG. 5 presents a flowchart illustrating how indexers process, index,and store data received from forwarders in accordance with one or moreaspects of the present disclosure;

FIG. 6 presents a flowchart illustrating how a search head and indexersperform a search query in accordance with one or more aspects of thepresent disclosure;

FIG. 7 presents a block diagram of a system for processing searchrequests that uses extraction rules for field values in accordance withone or more aspects of the present disclosure;

FIG. 8 illustrates an exemplary search query received from a client andexecuted by search peers in accordance with one or more aspects of thepresent disclosure;

FIG. 9A illustrates a search screen in accordance with one or moreaspects of the present disclosure;

FIG. 9B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with one or more aspects of thepresent disclosure;

FIG. 10A illustrates a key indicators view in accordance with one ormore aspects of the present disclosure;

FIG. 10B illustrates an incident review dashboard in accordance with oneor more aspects of the present disclosure;

FIG. 10C illustrates a proactive monitoring tree in accordance with oneor more aspects of the present disclosure;

FIG. 10D illustrates a screen displaying both log data and performancedata in accordance with one or more aspects of the present disclosure;

FIG. 11 depicts a block diagram of an example computing device operatingin accordance with one or more aspects of the present disclosure.

DETAILED DESCRIPTION

Disclosed herein are systems and methods for assigning scores to objectsbased on evaluating triggering conditions applied to datasets producedby search queries.

An example data aggregation and analysis system may aggregateheterogeneous machine-generated data received from various sources,including servers, databases, applications, networks, etc. Theaggregated source data may comprise a plurality of events. An event maybe represented by a data structure that is associated with a certainpoint in time and comprises a portion of raw machine data (i.e.,machine-generated data). The system may be configured to performreal-time indexing of the source data and to execute real-time,scheduled, or historic searches on the source data. A search query maycomprise one or more search terms specifying the search criteria. Searchterms may include keywords, phrases, Boolean expressions, regularexpressions, field names, name-value pairs, etc. The search criteria maycomprise a filter specifying relative or absolute time values, to limitthe scope of the search by a specific time value or a specific timerange.

The example data aggregation and analysis system executing a searchquery may evaluate the data relative to the search criteria by applyinga late binding schema (described further below) to produce a resultingdataset. The resulting dataset may comprise one or more data itemsrepresenting one or more portions of the source data that satisfy thesearch criteria.

The example data aggregation and analysis system may be employed toassign scores to various objects associated with a distributed computersystem (e.g., an enterprise system comprising a plurality of computersystems and peripheral devices interconnected by a plurality ofnetworks). An object may be represented, for example, by an entity (suchas a user or an organization), or an asset (such as a computer system oran application). In various illustrative examples, the scores assignedby the data aggregation and analysis system may represent security riskscores, system performance scores, or application performance scores. Incertain implementations, the scores assigned by the data aggregation andanalysis system may belong to a certain scale. Alternatively, the scoresmay be represented by values which do not belong to any scale. Incertain implementations, the scores may be represented by dimensionlessvalues. In certain implementations, a “less than” (or “greater than”)relationship may be defined among the values representing the scores.

In certain implementations, the data aggregation and analysis system mayadjust, by a certain score modifier value, a risk score assigned to acertain object responsive to determining that at least a portion of adataset produced by executing a search query satisfies a certaintriggering condition. A triggering condition may be applied to a datasetproduced by a search query that is executed by the system either in realtime or according to a certain schedule. Whenever at least a portion ofthe dataset returned by the search satisfies the triggering condition, arisk score associated with a certain object to which the portion of thedataset pertains (e.g., an object that is directly or indirectlyreferenced by the portion of the dataset) may be modified (increased ordecreased) by a certain risk score modifier value.

In an illustrative example, the risk score associated with an object maybe modified for every matching result (e.g., a data item) returned bythe search query. Alternatively, the risk score associated with anobject may be modified once for a certain number of matching resultsreturned by the search query.

The risk score modifier value may be determined based on values of oneor more fields of the portion of the dataset that has triggered the riskscore modification, as described in more details herein below.

The data aggregation and analysis system may be further configured topresent the assigned risk scores via a graphical user interface (GUI) ofa client computing device (e.g., a desktop computing device or a mobilecomputing device), as described in more details herein below.

Accordingly, implementations of the present disclosure provide aneffective mechanism for managing IT security, IT operations, and otheraspects of functioning of distributed computer systems by adjustingscores (e.g., security risk scores of performance scores) of objects inresponse to detecting occurrence of certain events. The adjusted scoresof objects are then visually presented to a user such as a systemadministrator to allow the user to quickly identify objects with respectto which certain remedial actions should be taken.

Various aspects of the methods and systems are described herein by wayof examples, rather than by way of limitation. The methods describedherein may be implemented by hardware (e.g., general purpose and/orspecialized processing devices, and/or other devices and associatedcircuitry), software (e.g., instructions executable by a processingdevice), or a combination thereof.

FIG. 1 schematically illustrates an example GUI for specifying securityscore modification rules, including search queries, triggeringconditions, and other information to be utilized by the system forassigning and/or modifying security risk scores associated with variousobjects, in accordance with one or more aspects of the presentdisclosure. While FIG. 1 and the corresponding description illustrateand refer to security risk scores, same and/or similar GUI elements,systems and methods may be utilized by the example data aggregation andanalysis system for specifying data searches, triggering conditions, andother information to be utilized by the system for assigning other typesof scores, such as system performance scores or application performancescores. System or application performance scores may be utilized forquantifying various aspects of system or application performance, e.g.,in situations when no single objectively measurable attribute orcharacteristic may reasonably be employed for the stated purpose.

As schematically illustrated by FIG. 1, example GUI 100 may comprise oneor more input fields for specifying search identifiers such as analphanumeric name 107 and an alphanumeric description 110 of thesecurity score modification rule defined by the search. Example GUI 100may further comprise a drop-down list for selecting the applicationcontext 115 associated with the search. In an illustrative example, theapplication context may identify an application of a certain platform,such as SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, Calif., which is described in more details herein below).

In certain implementations, example GUI 100 may further comprise a textbox 120 for specifying a search query string comprising one or moresearch terms specifying the search criteria. The search query string maycomply with the syntax of a certain query language supported by the dataaggregation and retrieval system, such as Splunk Search ProcessingLanguage (SPL) which is further described herein below. Alternatively,the search query may be specified using other input mechanisms, such asselecting the search query from a list of pre-defined search queries, orbuilding the search query using a wizard comprising a plurality ofpre-defined input fields.

Example GUI 100 may further comprise a start time and end time inputfields 125A-125B. In an illustrative example, the start time and endtime may define a time window specified relative to the current time(e.g., from 5 minutes before the current time to the current time). Thestart time and end time input fields specify the time range limiting thescope of the search, i.e., instructing the example data aggregation andanalysis system to perform the search query on the source data items(e.g., events) that have timestamps falling within the specified timerange.

Example GUI 100 may further comprise a schedule input field 130 todefine the schedule according to which the search query should beexecuted by the example data aggregation and analysis system. Theschedule may be represented by a data structure comprising values of oneor more scheduling parameters (e.g., minute, hour, day, month, and/orday-of-week). Executing search query according to a certain schedule maybe useful, e.g., for a search query that has its scope limited by a timewindow specified relative to the current time (e.g., from 5 minutesbefore the current time to the current time).

Example GUI 100 may further comprise a throttling window input field 135and a grouping field selection field 140 to define a throttlingcondition. The throttling condition may be utilized to suppress, for acertain period of time (e.g., for a number of seconds specified by field135), triggering the score modification and/or other actions associatedwith the search query. Grouping field 140 may be utilized to select afield by the value of which the search results should be grouped forevaluating the throttling condition. In other words, the example dataaggregation and analysis system may suppress the actions associated withthe search query for a specified number of seconds for the searchresults that include the same value in the specified field (e.g., thesame user identifier in the “user” field shown in the grouping field 140in the illustrative example of FIG. 1).

Example GUI 100 may further comprise a “Create risk score modifier”checkbox 145 specifying that the specified risk score modificationactions should be performed based on the results produced by the searchquery.

As noted herein above, the data aggregation and analysis system may beconfigured to adjust, by a certain risk score modifier value, the riskscore assigned to one or more objects responsive to determining that atleast a portion of a dataset produced by the search satisfies aparticular triggering condition. In an illustrative example, the riskscore associated with an object may be modified for every matchingresult (e.g., a data item) returned by the search query. Alternatively,the risk score associated with an object may be modified once for acertain number of matching results returned by the search query.

In the illustrative example of FIG. 1, the risk score modifier value isspecified by input field 150 as a constant integer value. Alternatively,the risk score modifier value may be determined by performing certaincalculations on one or more data items (referenced by the correspondingfields names) comprised by the resulting dataset produced by the searchquery. Risk score modifiers may be provided by positive or negativevalues. A positive risk score modifier value may indicate that the totalrisk score associated with an object should be increased (e.g., if theobject represents a user who has been engaged in an activity associatedwith an elevated risk score value). A negative risk score modifier valuemay indicate that the total risk score associated with an object shouldbe decreased (e.g., if the object represents a system administrator whohas been engaged in an activity that, if performed by a non-privilegeduser, would appear as associated with an elevated risk score value).

In an illustrative example, each occurrence of a certain pre-definedstate or situation may necessitate modifying a risk score assigned to anobject by a certain integer value. The arithmetic expression definingthe risk score modifier may specify that the integer value should bemultiplied by the number of occurrences of the state or situationreturned by the search query (e.g., if a failed login attempt increasesa user's risk score by 10, the arithmetic expression defining the riskscore modifier may specify the value being equal to 10*N, wherein N isthe number of failed login attempts). In another illustrative example,the risk score modifier may be proportional to a metric associated witha certain activity (e.g., if each kilobyte of VPN traffic increases theuser's risk score by 12, the arithmetic expression defining the riskscore modifier may specify the value being equal to 12*T/1024, wherein Tis the amount of VPN traffic, in bytes, associated with the user, and1024 is the number of bytes in a kilobyte).

Example GUI 100 may further comprise a risk object field 155 to identifythe object whose risk score should be modified by the example dataaggregation and analysis system. The risk object may be identified by adata item (referenced by the field name 155) comprised by a datasetproduced by the search query. Example objects may include a user, acomputer system, a network, an application, etc.

In certain implementations, should the identified field name contain anempty value, the example data aggregation and analysis system may applythe risk score modifier to the risk score associated with a pre-definedobject (e.g., a fictitious object). In an illustrative example, thefictitious object to which risk score modifiers associated withunidentified objects are applied may be referenced by a symbolic name(e.g., UNKNOWN object). Applying risk score modifiers associated withunidentified objects to a fictitious object may be utilized to attract auser's attention to the fact that certain objects associated withnon-zero (or even significant) risk scores could not be identified bythe system.

Example GUI 100 may further comprise a risk object type field 160 toidentify the type of risk object 155. In various illustrative examples,the risk object type may be represented by one of the following types:an entity (such as a user or an organization), an asset (such as acomputer system or an application), or a user-defined type.

Example GUI 100 may further comprise one or more action check-boxes165A-165C to specify one or more actions to be performed by the systemresponsive to determining that at least a portion of the datasetproduced by executing the specified search query satisfies the specifiedtriggering condition. The actions may include, for example, sending ane-mail message comprising the risk score modifier value and/or at leastpart of the dataset that has triggered the risk score modification,creating an RSS feed comprising the risk score modifier value and/or atleast part of the dataset that has triggered the risk scoremodification, and/or executing a shell script having at least oneparameter defined based on the score.

In certain implementations, the specified actions may be performed withrespect to each result produced by the search query defined by queryinput field 110 (in other words, the simplest triggering condition isapplied to the resulting dataset requiring that the resulting datasetcomprise a non-zero number of results). Alternatively, an additionaltriggering condition may be applied to the resulting dataset produced bythe search query (e.g., comparing the number of data items in theresulting dataset produced to a certain configurable integer value orperforming a secondary search on the dataset produced by executing thesearch query).

In certain implementations, responsive to modifying a score assigned tothe primary object, the example data aggregation and analysis system mayalso modify scores assigned to one or more additional objects which areassociated with the primary object. For example, if security risk scoreassigned to a user is modified responsive to a certain triggeringcondition, the system may further modify the security risk scoreassigned to the user's computer. In an illustrative example, the exampledata aggregation and analysis system may identify one or more additionalobjects associated with the primary objects based on one or more objectassociation rules. In another illustrative example, the example dataaggregation and analysis system may identify one or more additionalobjects associated with the primary objects based on performing asecondary search using a pre-defined or dynamically constructed searchquery. The risk score modifier value to be applied to the associatedadditional object may be determined based on the risk score modifiervalue of the primary object and/or one or more object association rules.In an illustrative example, an object association rule may specify thatthe risk score modifier value of an additional object (e.g., a computer)associated with a primary object (e.g., a user) may be determined as acertain fraction of the risk score modifier value of the primary object.

As noted herein above, the example data aggregation and analysis systemmay be further configured to present the assigned security risk scoresvia a graphical user interface (GUI) of a client computing device (e.g.,a desktop computing device or a mobile computing device). FIG. 2schematically illustrates an example GUI for visually presentingsecurity risk scores assigned to a plurality of objects, in accordancewith one or more aspects of the present disclosure. While FIG. 2 and thecorresponding description illustrate and refer to security risk scores,same and/or similar GUI elements, systems and methods may be utilized bythe example data aggregation and analysis system for visually presentingother types of scores, such as system performance scores or applicationperformance scores.

As schematically illustrated by FIG. 2, example GUI 200 may compriseseveral panels 210A-210N to dynamically present graphical and/or textualinformation associated with security risk scores. In the illustrativeexample of FIG. 2, example GUI 200 may further comprise a panel 210Ashowing a graph 232 representing the total risk score value assigned toa selected set of objects within the time period identified by timeperiod selection dropdown control 234. The set of objects for displayingthe risk score values may be specified by the risk object identifier(input field 236), and/or risk object type (input field 238). The riskscore values may be further filtered by specifying the risk objectsources (e.g., risk score modification rules) via input field 240.

Example GUI 200 may further comprise panel 210B representing, in arectangular table, risk scores (column 242) assigned to a plurality ofobjects identified by symbolic names (column 244). The set of objectsfor which the scores are displayed and/or the risk scores to bedisplayed may be limited by one or more parameters specified by one ormore fields of the input panel 210A.

The table entries displayed within input panel 210B may be sorted, e.g.,in a descending order of total risk score associated with thecorresponding object, thus allowing the user to focus on the objectsassociated with the largest values of risk security scores. Panel 210Bmay further comprise column 246 showing the object type (e.g., a usertype, a system type, or a user-defined type). Panel 200A may furthercomprise column 248 showing the number of various sources (e.g., riskscore modification rules) contributing to the total risk scoreassociated with the object identified by column 242, and column 250showing the number of individual risk score modifiers reflected by thetotal risk score associated with the object identified by column 242.

Example GUI 200 may further comprise panel 210C representing, in arectangular table, aggregate risk score values grouped by sources (e.g.,risk score modification rules identified by symbolic names in column212) and ordered in the descending order of the risk score value (column214). Panel 210C may further comprise column 216 showing the number ofobjects having their risk score values modified by the correspondingsource, and column 218 showing the number of individual risk scoremodifiers reflected by the total risk score value identified by column214.

Example GUI 200 may further comprise a panel 210N representing, in arectangular table, most recent risk modifiers (column 220) associatedwith various objects identified by column 222. The table entries may beordered in the reverse time order (most recent entries first) of therisk modifier creation time (column 224). Panel 210N may furthercomprise column 226 showing the object type, column 228 showing the riskmodifier source (e.g., a risk score modification rule identified by asymbolic name), and column 230 showing the risk modifier description.

In certain implementations, the example data aggregation and analysissystem may allow a user to “drill down” to the underlying data that hastriggered a particular risk score modifier. For example, responsive toreceiving the user's selection of a particular risk score modifier, thesystem may display further information pertaining to the selectedmodifier, including the underlying portion of the dataset that hastriggered the risk score modifier.

In certain implementations, the example data aggregation and analysissystem may provide an “ad hoc” score modification interface to allow auser to adjust risk score modifiers assigned to certain objects. In anillustrative example, a user may increase or decrease a risk score valueassigned to a certain object or a group of objects.

FIGS. 3A-3C depict flow diagrams of example methods 300A-300B forassigning scores to objects based on evaluating triggering conditionsapplied to datasets produced by search queries. Methods 300A-300B and/oreach of their respective individual functions, routines, subroutines, oroperations may be performed by one or more general purpose and/orspecialized processing devices. Two or more functions, routines,subroutines, or operations of methods 300A-300B may be performed inparallel or in an order that may differ from the order described above.In certain implementations, one or more of methods 300A-300B may beperformed by a single processing thread. Alternatively, methods300A-300B may be performed by two or more processing threads, eachthread executing one or more individual functions, routines,subroutines, or operations of the respective method. In an illustrativeexample, the processing threads implementing methods 300A-300B may besynchronized (e.g., using semaphores, critical sections, and/or otherthread synchronization mechanisms). Alternatively, the processingthreads implementing methods 300A-300B may be executed asynchronouslywith respect to each other. In an illustrative example, methods300A-300B may be performed by an example computing device 1000 describedherein below with references to FIG. 11. In another illustrativeexample, methods 300A-300B may be performed by a distributed computersystem comprising two or more example computing devices 1000.

FIG. 3A depict a flow diagram of an example method 300A for modifyingscore values assigned to certain objects based on search query results,in accordance with one or more aspects of the present disclosure.

Referring to FIG. 3A, at block 310, the computer system implementing themethod may execute a search query. In an illustrative example, thesearch query may represent a real-time search (e.g., may repeatedly beexecuted by a certain process or thread in an indefinite loop which maybe interrupted by occurrences of certain terminating conditions). Inanother illustrative example, the search query may represent a scheduledsearch (e.g., may be executed according to a certain schedule), asdescribed in more details herein above.

Responsive to determining, at block 315, that a portion of the datasetproduced by the search query satisfies a triggering condition defined bya risk score modification rule associated with the search query, theprocessing may continue at block 320; otherwise, the processingassociated with the current search query instance may terminate.

At block 320, the computer system may modify a risk score value of acertain primary object by a risk score modifier value. The primaryobject may be identified based on values of one or more fields of theportion of the dataset returned by the search query, in accordance withthe risk score modification rule associated with the search query, asdescribed in more details herein above. The risk score modifier valuesmay be determined in accordance with the risk score modification ruleassociated with the search query. In an illustrative example, the riskscore modifier value applicable to a certain object may be defined as aconstant integer value. Alternatively, the risk score modifier value maybe determined by performing certain calculations on one or more dataitems (referenced by the corresponding fields names) comprised by theresulting dataset produced by the search query. In an illustrativeexample, the risk score modifier value may be specified by a certainarithmetic expression. The arithmetic expression may comprise one ormore arithmetic operations to be performed on two or more operands. Eachof the operands may be represented by a value of a data item (referencedby the corresponding fields name) comprised by the resulting datasetproduced by the search query or by certain constant value.

At block 330, the computer system may modify risk score values ofcertain objects associated with the primary object. The example dataaggregation and analysis system may identify one or more objectsassociated with the primary objects based on one or more objectassociation rules. The risk score modifier value to be applied to theassociated additional object may be determined based on the risk scoremodifier value of the primary object and/or one or more objectassociation rules, as described in more details herein above withreferences to FIG. 1.

FIG. 3B depicts a flow diagram of an example method 300B for presentingscore modifier information, in accordance with one or more aspects ofthe present disclosure. As noted herein above, method 300B may beimplemented by a server (e.g., a presentation server) and/or by one ormore clients of the distributed computer system operating in accordancewith one or more aspects of the present disclosure.

Referring to FIG. 3B, at block 350, the computer system implementing themethod may sort the score modifier information associated with certainobjects in an order reflecting the corresponding score modifier values(e.g., in the descending order of the score modifier values). Theobjects for displaying the associated score modifier information may beselected by a user via a GUI, as described in more details herein abovewith references to FIG. 2.

At block 355, the computer system may cause the score modifierinformation to be displayed by a client computing device, as describedin more details herein above with references to FIG. 2.

Responsive to receiving, at block 360, a user's selection of aparticular score modifier of the displayed score modifiers, the computersystem may, at block 365, cause further information pertaining to theselected modifier to be displayed, including the underlying portion ofthe dataset that has triggered the risk score modifier.

The systems and methods described herein above may be employed byvarious data processing systems, e.g., data aggregation and analysissystems. In certain implementations, the example data aggregation andanalysis system may perform search queries on performance data thatstored as “events,” wherein each event comprises a collection ofperformance data and/or diagnostic information that is generated by acomputer system and is correlated with a specific point in time. Invarious illustrative examples, the data processing system may berepresented by the SPLUNK® ENTERPRISE system produced by Splunk Inc. ofSan Francisco, Calif., to store and process performance data. The dataprocessing system may be configured to execute search queries ascorrelational searches, as described in more details herein below. Incertain implementations, the data processing system may be configured toexecute certain functions described herein with respect to SPLUNK® APPFOR ENTERPRISE SECURITY.

Modern data centers often comprise thousands of host computer systemsthat operate collectively to service requests from even larger numbersof remote clients. During operation, these data centers generatesignificant volumes of performance data and diagnostic information thatcan be analyzed to quickly diagnose performance problems. In order toreduce the size of this performance data, the data is typicallypre-processed prior to being stored based on anticipated data-analysisneeds. For example, pre-specified data items can be extracted from theperformance data and stored in a database to facilitate efficientretrieval and analysis at search time. However, the rest of theperformance data is not saved and is essentially discarded duringpre-processing. As storage capacity becomes progressively cheaper andmore plentiful, there are fewer incentives to discard this performancedata and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed performance data at “ingestiontime” for later retrieval and analysis at “search time.” Note thatperforming the analysis operations at search time provides greaterflexibility because it enables an analyst to search all of theperformance data, instead of searching pre-specified data items thatwere stored at ingestion time. This enables the analyst to investigatedifferent aspects of the performance data instead of being confined tothe pre-specified set of data items that were selected at ingestiontime.

However, analyzing massive quantities of heterogeneous performance dataat search time can be a challenging task. A data center may generateheterogeneous performance data from thousands of different components,which can collectively generate tremendous volumes of performance datathat can be time-consuming to analyze. For example, this performancedata can include data from system logs, network packet data, sensordata, and data generated by various applications. Also, the unstructurednature of much of this performance data can pose additional challengesbecause of the difficulty of applying semantic meaning to unstructureddata, and the difficulty of indexing and querying unstructured datausing traditional database systems.

These challenges can be addressed by using an event-based system, suchas the SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, Calif., to store and process performance data. The SPLUNK®ENTERPRISE system is the leading platform for providing real-timeoperational intelligence that enables organizations to collect, index,and harness machine-generated data from various websites, applications,servers, networks, and mobile devices that power their businesses. TheSPLUNK® ENTERPRISE system is particularly useful for analyzingunstructured performance data, which is commonly found in system logfiles. Although many of the techniques described herein are explainedwith reference to the SPLUNK® ENTERPRISE system, the techniques are alsoapplicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” wherein each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time series data,” wherein time series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, whereinspecific data items with specific data formats reside at predefinedlocations in the data. For example, structured data can include dataitems stored in fields in a database table. In contrast, unstructureddata does not have a predefined format. This means that unstructureddata can comprise various data items having different data types thatcan reside at different locations. For example, when the data source isan operating system log, an event can include one or more lines from theoperating system log containing raw data that includes different typesof performance and diagnostic information associated with a specificpoint in time. Examples of data sources from which an event may bederived include, but are not limited to: web servers; applicationservers; databases; firewalls; routers; operating systems; and softwareapplications that execute on computer systems, mobile devices, andsensors. The data generated by such data sources can be produced invarious forms including, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements and sensor measurements. An eventtypically includes a timestamp that may be derived from the raw data inthe event, or may be determined through interpolation between temporallyproximate events having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, wherein theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly,” when it isneeded (e.g., at search time), rather than at ingestion time of the dataas in traditional database systems. Because the schema is not applied toevent data until it is needed (e.g., at search time), it is referred toas a “late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained fromweblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce timestampedevents. The system stores the timestamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction rulecomprises a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timea query is actually executed. This means that extraction rules for thefields in a query may be provided in the query itself, or may be locatedduring execution of the query. Hence, as an analyst learns more aboutthe data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques.

Also, a number of “default fields” that specify metadata about theevents rather than data in the events themselves can be createdautomatically. For example, such default fields can specify: a timestampfor the event data; a host from which the event data originated; asource of the event data; and a source type for the event data. Thesedefault fields may be determined automatically when the events arecreated, indexed or stored.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by different data sources, thesystem facilitates use of a “common information model” (CIM) across thedifferent data sources.

FIG. 4 presents a block diagram of an exemplary event-processing system100, similar to the SPLUNK® ENTERPRISE system. System 100 includes oneor more forwarders 101 that collect data obtained from a variety ofdifferent data sources 105, and one or more indexers 102 that store,process, and/or perform operations on this data, wherein each indexeroperates on data contained in a specific data store 103. Theseforwarders and indexers can comprise separate computer systems in a datacenter, or may alternatively comprise separate processes executing onvarious computer systems in a data center.

During operation, the forwarders 101 identify which indexers 102 willreceive the collected data and then forward the data to the identifiedindexers. Forwarders 101 can also perform operations to strip outextraneous data and detect timestamps in the data. The forwarders nextdetermine which indexers 102 will receive each data item and thenforward the data items to the determined indexers 102.

Note that distributing data across different indexers facilitatesparallel processing. This parallel processing can take place at dataingestion time, because multiple indexers can process the incoming datain parallel. The parallel processing can also take place at search time,because multiple indexers can search through the data in parallel.

System 100 and the processes described below with respect to FIGS. 5-10are further described in “Exploring Splunk Search Processing Language(SPL) Primer and Cookbook” by David Carasso, CITO Research, 2012, and in“Optimizing Data Analysis With a Semi-Structured Time Series Database”by Ledion Bitincka, Archana Ganapathi, Stephen Sorkin, and Steve Zhang,SLAML, 2010, each of which is hereby incorporated herein by reference inits entirety for all purposes.

FIG. 5 presents a flowchart illustrating how an indexer processes,indexes, and stores data received from forwarders in accordance with thedisclosed embodiments. At block 201, the indexer receives the data fromthe forwarder. Next, at block 202, the indexer apportions the data intoevents. Note that the data can include lines of text that are separatedby carriage returns or line breaks and an event may include one or moreof these lines. During the apportioning process, the indexer can useheuristic rules to automatically determine the boundaries of the events,which for example coincide with line boundaries. These heuristic rulesmay be determined based on the source of the data, wherein the indexercan be explicitly informed about the source of the data or can infer thesource of the data by examining the data. These heuristic rules caninclude regular expression-based rules or delimiter-based rules fordetermining event boundaries, wherein the event boundaries may beindicated by predefined characters or character strings. Thesepredefined characters may include punctuation marks or other specialcharacters including, for example, carriage returns, tabs, spaces orline breaks. In some cases, a user can fine-tune or configure the rulesthat the indexers use to determine event boundaries in order to adaptthe rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 203. Asmentioned above, these timestamps can be determined by extracting thetime directly from data in the event, or by interpolating the time basedon timestamps from temporally proximate events. In some cases, atimestamp can be determined based on the time the data was received orgenerated. The indexer subsequently associates the determined timestampwith each event at block 204, for example by storing the timestamp asmetadata for each event.

Then, the system can apply transformations to data to be included inevents at block 205. For log data, such transformations can includeremoving a portion of an event (e.g., a portion used to define eventboundaries, extraneous text, characters, etc.) or removing redundantportions of an event. Note that a user can specify portions to beremoved using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fastkeyword searching for events. To build a keyword index, the indexerfirst identifies a set of keywords in block 206. Then, at block 207 theindexer includes the identified keywords in an index, which associateseach stored keyword with references to events containing that keyword(or to locations within events where that keyword is located). When anindexer subsequently receives a keyword-based query, the indexer canaccess the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, wherein a name-value pair can includea pair of keywords connected by a symbol, such as an equals sign orcolon. In this way, events containing these name-value pairs can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the name-value pairs at the time ofindexing. For example, if the string “dest=10.0.1.2” is found in anevent, a field named “dest” may be created for the event, and assigned avalue of “10.0.1.2.”

Finally, the indexer stores the events in a data store at block 208,wherein a timestamp can be stored with each event to facilitatesearching for events based on a time range. In some cases, the storedevents are organized into a plurality of buckets, wherein each bucketstores events associated with a specific time range. This not onlyimproves time-based searches, but it also allows events with recenttimestamps that may have a higher likelihood of being accessed to bestored in faster memory to facilitate faster retrieval. For example, abucket containing the most recent events can be stored as flash memoryinstead of on hard disk.

Each indexer 102 is responsible for storing and searching a subset ofthe events contained in a corresponding data store 103. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel, for example using map-reduce techniques,wherein each indexer returns partial responses for a subset of events toa search head that combines the results to produce an answer for thequery. By storing events in buckets for specific time ranges, an indexermay further optimize searching by looking only in buckets for timeranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as is described in U.S. patent application Ser. No. 14/266,812filed on 30 Apr. 2014, and in U.S. patent application Ser. No.14/266,817 also filed on 30 Apr. 2014.

FIG. 6 presents a flowchart illustrating how a search head and indexersperform a search query in accordance with the disclosed embodiments. Atthe start of this process, a search head receives a search query from aclient at block 301. Next, at block 302, the search head analyzes thesearch query to determine what portions can be delegated to indexers andwhat portions need to be executed locally by the search head. At block303, the search head distributes the determined portions of the query tothe indexers. Note that commands that operate on single events can betrivially delegated to the indexers, while commands that involve eventsfrom multiple indexers are harder to delegate.

Then, at block 304, the indexers to which the query was distributedsearch their data stores for events that are responsive to the query. Todetermine which events are responsive to the query, the indexer searchesfor events that match the criteria specified in the query. This criteriacan include matching keywords or specific values for certain fields. Ina query that uses a late-binding schema, the searching operations inblock 304 may involve using the late-binding scheme to extract valuesfor specified fields from events at the time the query is processed.Next, the indexers can either send the relevant events back to thesearch head, or use the events to calculate a partial result, and sendthe partial result back to the search head.

Finally, at block 305, the search head combines the partial resultsand/or events received from the indexers to produce a final result forthe query. This final result can comprise different types of datadepending upon what the query is asking for. For example, the finalresults can include a listing of matching events returned by the query,or some type of visualization of data from the returned events. Inanother example, the final result can include one or more calculatedvalues derived from the matching events.

Moreover, the results generated by system 100 can be returned to aclient using different techniques. For example, one technique streamsresults back to a client in real-time as they are identified. Anothertechnique waits to report results to the client until a complete set ofresults is ready to return to the client. Yet another technique streamsinterim results back to the client in real-time until a complete set ofresults is ready, and then returns the complete set of results to theclient. In another technique, certain results are stored as “searchjobs,” and the client may subsequently retrieve the results byreferencing the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head starts executing aquery, the search head can determine a time range for the query and aset of common keywords that all matching events must include. Next, thesearch head can use these parameters to query the indexers to obtain asuperset of the eventual results. Then, during a filtering stage, thesearch head can perform field-extraction operations on the superset toproduce a reduced set of search results.

FIG. 7 presents a block diagram illustrating how fields can be extractedduring query processing in accordance with the disclosed embodiments. Atthe start of this process, a search query 402 is received at a queryprocessor 404. Query processor 404 includes various mechanisms forprocessing a query, wherein these mechanisms can reside in a search head104 and/or an indexer 102. Note that the exemplary search query 402illustrated in FIG. 7 is expressed in Search Processing Language (SPL),which is used in conjunction with the SPLUNK® ENTERPRISE system. SPL isa pipelined search language in which a set of inputs is operated on by afirst command in a command line, and then a subsequent command followingthe pipe symbol “|” operates on the results produced by the firstcommand, and so on for additional commands. Search query 402 can also beexpressed in other query languages, such as the Structured QueryLanguage (“SQL”) or any suitable query language.

Upon receiving search query 402, query processor 404 sees that searchquery 402 includes two fields “IP” and “target.” Query processor 404also determines that the values for the “IP” and “target” fields havenot already been extracted from events in data store 414, andconsequently determines that query processor 404 needs to use extractionrules to extract values for the fields. Hence, query processor 404performs a lookup for the extraction rules in a rule base 406, whereinrule base 406 maps field names to corresponding extraction rules andobtains extraction rules 408-409, wherein extraction rule 408 specifieshow to extract a value for the “IP” field from an event, and extractionrule 409 specifies how to extract a value for the “target” field from anevent. As is illustrated in FIG. 7, extraction rules 408-409 cancomprise regular expressions that specify how to extract values for therelevant fields. Such regular-expression-based extraction rules are alsoreferred to as “regex rules.” In addition to specifying how to extractfield values, the extraction rules may also include instructions forderiving a field value by performing a function on a character string orvalue retrieved by the extraction rule. For example, a transformationrule may truncate a character string, or convert the character stringinto a different data format. In some cases, the query itself canspecify one or more extraction rules.

Next, query processor 404 sends extraction rules 408-409 to a fieldextractor 412, which applies extraction rules 408-409 to events 416-418in a data store 414. Note that data store 414 can include one or moredata stores, and extraction rules 408-409 can be applied to largenumbers of events in data store 414, and are not meant to be limited tothe three events 416-418 illustrated in FIG. 7. Moreover, the queryprocessor 404 can instruct field extractor 412 to apply the extractionrules to all the events in a data store 414, or to a subset of theevents that have been filtered based on some criteria.

Next, field extractor 412 applies extraction rule 408 for the firstcommand “Search IP=“10*” to events in data store 414 including events416-418. Extraction rule 408 is used to extract values for the IPaddress field from events in data store 414 by looking for a pattern ofone or more digits, followed by a period, followed again by one or moredigits, followed by another period, followed again by one or moredigits, followed by another period, and followed again by one or moredigits. Next, field extractor 412 returns field values 420 to queryprocessor 404, which uses the criterion IP=“10*” to look for IPaddresses that start with “10”. Note that events 416 and 417 match thiscriterion, but event 418 does not, so the result set for the firstcommand is events 416-417.

Query processor 404 then sends events 416-417 to the next command “statscount target.” To process this command, query processor 404 causes fieldextractor 412 to apply extraction rule 409 to events 416-417. Extractionrule 409 is used to extract values for the target field for events416-417 by skipping the first four commas in events 416-417, and thenextracting all of the following characters until a comma or period isreached. Next, field extractor 412 returns field values 421 to queryprocessor 404, which executes the command “stats count target” to countthe number of unique values contained in the target fields, which inthis example produces the value “2” that is returned as a final result422 for the query.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include: a set of one or more events; a set of one or morevalues obtained from the events; a subset of the values; statisticscalculated based on the values; a report containing the values; or avisualization, such as a graph or chart, generated from the values.

FIG. 9A illustrates an exemplary search screen 600 in accordance withthe disclosed embodiments. Search screen 600 includes a search bar 602that accepts user input in the form of a search string. It also includesa time range picker 612 that enables the user to specify a time rangefor the search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 9B that enables the user to selectdifferent sources for the event data, for example by selecting specifichosts and log files.

After the search is executed, the search screen 600 can display theresults through search results tabs 604, wherein search results tabs 604includes: an “events tab” that displays various information about eventsreturned by the search; a “statistics tab” that displays statisticsabout the search results; and a “visualization tab” that displaysvarious visualizations of the search results. The events tab illustratedin FIG. 9A displays a timeline graph 605 that graphically illustratesthe number of events that occurred in one-hour intervals over theselected time range. It also displays an events list 608 that enables auser to view the raw data in each of the returned events. Itadditionally displays a fields sidebar 606 that includes statisticsabout occurrences of specific fields in the returned events, including“selected fields” that are pre-selected by the user, and “interestingfields” that are automatically selected by the system based onpre-specified criteria.

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed performancedata “on the fly” at search time instead of storing pre-specifiedportions of the performance data in a database at ingestion time. Thisflexibility enables a user to see correlations in the performance dataand perform subsequent queries to examine interesting aspects of theperformance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause considerable delays whileprocessing the queries. Fortunately, a number of acceleration techniqueshave been developed to speed up analysis operations performed at searchtime. These techniques include: (1) performing search operations inparallel by formulating a search as a map-reduce computation; (2) usinga keyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These techniques aredescribed in more detail below.

To facilitate faster query processing, a query can be structured as amap-reduce computation, wherein the “map” operations are delegated tothe indexers, while the corresponding “reduce” operations are performedlocally at the search head. For example, FIG. 8 illustrates how a searchquery 501 received from a client at search head 104 can split into twophases, including: (1) a “map phase” comprising subtasks 502 (e.g., dataretrieval or simple filtering) that may be performed in parallel and are“mapped” to indexers 102 for execution, and (2) a “reduce phase”comprising a merging operation 503 to be executed by the search headwhen the results are ultimately collected from the indexers.

During operation, upon receiving search query 501, search head 104modifies search query 501 by substituting “stats” with “prestats” toproduce search query 502, and then distributes search query 502 to oneor more distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as is illustrated in FIG. 6, or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head performs the merging operations 503 on theresults. Note that by executing the computation in this way, the systemeffectively distributes the computational operations while minimizingdata transfers.

As described above with reference to the flow charts in FIGS. 6 and 7,event-processing system 100 can construct and maintain one or morekeyword indices to facilitate rapidly identifying events containingspecific keywords. This can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

To speed up certain types of queries, some embodiments of system 100make use of a high performance analytics store, which is referred to asa “summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an exemplary entry in a summarization table can keep track ofoccurrences of the value “94107” in a “ZIP code” field of a set ofevents, wherein the entry includes references to all of the events thatcontain the value “94107” in the ZIP code field. This enables the systemto quickly process queries that seek to determine how many events have aparticular value for a particular field, because the system can examinethe entry in the summarization table to count instances of the specificvalue in the field without having to go through the individual events ordo extractions at search time. Also, if the system needs to process allevents that have a specific field-value combination, the system can usethe references in the summarization table entry to directly access theevents to extract further information without having to search all ofthe events to find the specific field-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range, wherein a bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer, wherein theindexer-specific summarization table only includes entries for theevents in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query”that scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A collection query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Acollection query can also be automatically launched in response to aquery that asks for a specific field-value combination.

In some cases, the summarization tables may not cover all of the eventsthat are relevant to a query. In this case, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.This summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. (This is possible if results from preceding timeperiods can be computed separately and combined to generate an updatedreport. In some cases, it is not possible to combine such incrementalresults, for example where a value in the report depends onrelationships between events from different time periods.) If reportscan be accelerated, the summarization engine periodically generates asummary covering data obtained during a latest non-overlapping timeperiod. For example, where the query seeks events meeting a specifiedcriteria, a summary for the time period includes only events within thetime period that meet the specified criteria. Similarly, if the queryseeks statistics calculated from the events, such as the number ofevents that match the specified criteria, then the summary for the timeperiod includes the number of events in the period that match thespecified criteria.

In parallel with the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so only the newer eventdata needs to be processed while generating an updated report. Thesereport acceleration techniques are described in more detail in U.S. Pat.No. 8,589,403, issued on Nov. 19, 2013, and U.S. Pat. No. 8,412,696,issued on Apr. 2, 2011.

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that make it easy for developers to create applicationsto provide additional capabilities. One such application is the SPLUNK®APP FOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. This differs significantly fromconventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related event data. Traditional SIEM systemstypically use fixed schemas to extract data from pre-definedsecurity-related fields at data ingestion time, wherein the extracteddata is typically stored in a relational database. This data extractionprocess (and associated reduction in data size) that occurs at dataingestion time inevitably hampers future incident investigations, whenall of the original data may be needed to determine the root cause of asecurity issue, or to detect the tiny fingerprints of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. (The process of detecting securitythreats for network-related information is further described in U.S.patent application Ser. No. 13/956,252, and Ser. No. 13/956,262.)Security-related information can also include endpoint information, suchas malware infection data and system configuration information, as wellas access control information, such as login/logout information andaccess failure notifications. The security-related information canoriginate from various sources within a data center, such as hosts,virtual machines, storage devices and sensors. The security-relatedinformation can also originate from various sources in a network, suchas routers, switches, email servers, proxy servers, gateways, firewallsand intrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting so-called “notable events” that are likely to indicate asecurity threat. These notable events can be detected in a number ofways: (1) an analyst can notice a correlation in the data and canmanually identify a corresponding group of one or more events as“notable;” or (2) an analyst can define a “correlation search”specifying criteria for a notable event, and every time one or moreevents satisfy the criteria, the application can indicate that the oneor more events are notable. An analyst can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics of interest, such as countsof different types of notable events. For example, FIG. 10A illustratesan exemplary key indicators view 700 that comprises a dashboard, whichcan display a value 701, for various security-related metrics, such asmalware infections 702. It can also display a change in a metric value703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 700 additionallydisplays a histogram panel 704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338filed Jul. 31, 2013.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 10B illustrates an exemplary incident review dashboard 710 thatincludes a set of incident attribute fields 711 that, for example,enables a user to specify a time range field 712 for the displayedevents. It also includes a timeline 713 that graphically illustrates thenumber of incidents that occurred in one-hour time intervals over theselected time range. It additionally displays an events list 714 thatenables a user to view a list of all of the notable events that matchthe criteria in the incident attributes fields 711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent. The incident review dashboard is described further in“http://docs.splunk.com/Documentation/PCI/2.1.1/

User/IncidentReviewdashboard.”

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that make it easy for developers to create variousapplications. One such application is the SPLUNK® APP FOR VMWARE®, whichperforms monitoring operations and includes analytics to facilitatediagnosing the root cause of performance problems in a data center basedon large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lackthe infrastructure to effectively store and analyze large volumes ofperformance information and log data obtained from the data center. Inconventional data-center-monitoring systems, this performance data istypically pre-processed prior to being stored, for example by extractingpre-specified data items from the performance data and storing them in adatabase to facilitate subsequent retrieval and analysis at search time.However, the rest of the performance data is not saved and isessentially discarded during pre-processing. In contrast, the SPLUNK®APP FOR VMWARE® stores large volumes of minimally processed performanceinformation and log data at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated.

The SPLUNK® APP FOR VMWARE® can process many types ofperformance-related information. In general, this performance-relatedinformation can include any type of performance-related data and logdata produced by virtual machines and host computer systems in a datacenter. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. For moredetails about such performance metrics, please see U.S. patent Ser. No.14/167,316 filed 29 Jan. 2014, which is hereby incorporated herein byreference. Also, see “vSphere Monitoring and Performance,” Update 1,vSphere 5.5, EN-001357-00,http://pubs.vmware.com/vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-monitoring-performance-guide.pdf.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Exemplary node-expansion operations are illustrated in FIG.10C, wherein nodes 733 and 734 are selectively expanded. Note that nodes731-739 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/235,490 filed on 15 Apr. 2014, which is hereby incorporated herein byreference for all possible purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous data,comprising events, log data and associated performance metrics, for theselected time range. For example, the screen illustrated in FIG. 10Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 742 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316 filed on 29Jan. 2014, which is hereby incorporated herein by reference for allpossible purposes.

FIG. 11 illustrates a diagrammatic representation of a computing device1000 within which a set of instructions for causing the computing deviceto perform the methods discussed herein may be executed. The computingdevice 1000 may be connected to other computing devices in a LAN, anintranet, an extranet, and/or the Internet. The computing device 1000may operate in the capacity of a server machine in client-server networkenvironment. The computing device 1000 may be provided by a personalcomputer (PC), a set-top box (STB), a server, a network router, switchor bridge, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single computing device is illustrated,the term “computing device” shall also be taken to include anycollection of computing devices that individually or jointly execute aset (or multiple sets) of instructions to perform the methods discussedherein. In illustrative examples, the computing device 1000 mayimplement the above described methods 300A-300B for assigning scores toobjects based on evaluating triggering conditions applied to datasetsproduced by search queries.

The example computing device 1000 may include a processing device (e.g.,a general purpose processor) 1002, a main memory 1004 (e.g., synchronousdynamic random access memory (DRAM), read-only memory (ROM)), a staticmemory 1006 (e.g., flash memory and a data storage device 1018), whichmay communicate with each other via a bus 1030.

The processing device 1002 may be provided by one or moregeneral-purpose processing devices such as a microprocessor, centralprocessing unit, or the like. In an illustrative example, the processingdevice 1002 may comprise a complex instruction set computing (CISC)microprocessor, reduced instruction set computing (RISC) microprocessor,very long instruction word (VLIW) microprocessor, or a processorimplementing other instruction sets or processors implementing acombination of instruction sets. The processing device 1002 may alsocomprise one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), a networkprocessor, or the like. The processing device 1002 may be configured toexecute the methods 300A-300B for assigning scores to objects based onevaluating triggering conditions applied to datasets produced by searchqueries, in accordance with one or more aspects of the presentdisclosure.

The computing device 1000 may further include a network interface device1008, which may communicate with a network 1020. The computing device1000 also may include a video display unit 1010 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device1012 (e.g., a keyboard), a cursor control device 1014 (e.g., a mouse)and an acoustic signal generation device 1016 (e.g., a speaker). In oneembodiment, video display unit 1010, alphanumeric input device 1012, andcursor control device 1014 may be combined into a single component ordevice (e.g., an LCD touch screen).

The data storage device 1018 may include a computer-readable storagemedium 1028 on which may be stored one or more sets of instructions(e.g., instructions of the methods 300A-300B for assigning scores toobjects based on evaluating triggering conditions applied to datasetsproduced by search queries, in accordance with one or more aspects ofthe present disclosure) implementing any one or more of the methods orfunctions described herein. Instructions implementing methods 300A-300Bmay also reside, completely or at least partially, within main memory1004 and/or within processing device 1002 during execution thereof bycomputing device 1000, main memory 1004 and processing device 1002 alsoconstituting computer-readable media. The instructions may further betransmitted or received over a network 1020 via network interface device1008.

While computer-readable storage medium 1028 is shown in an illustrativeexample to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database and/or associated cachesand servers) that store one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform the methods described herein. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, optical media and magnetic media.

Unless specifically stated otherwise, terms such as “updating,”“identifying,” “determining,” “sending,” “assigning,” or the like referto actions and processes performed or implemented by computing devicesthat manipulate and transform data represented as physical (electronic)quantities within the computing device's registers and memories intoother data similarly represented as physical quantities within thecomputing device memories or registers or other such informationstorage, transmission or display devices. Also, the terms “first,”“second,” “third,” “fourth,” etc. as used herein are meant as labels todistinguish among different elements and may not necessarily have anordinal meaning according to their numerical designation.

Examples described herein also relate to an apparatus for performing themethods described herein. This apparatus may be specially constructedfor the required purposes, or it may comprise a general purposecomputing device selectively programmed by a computer program stored inthe computing device. Such a computer program may be stored in acomputer-readable non-transitory storage medium.

The methods and illustrative examples described herein are notinherently related to any particular computer or other apparatus.Various general purpose systems may be used in accordance with theteachings described herein, or it may prove convenient to construct morespecialized apparatus to perform the required method operations. Therequired structure for a variety of these systems will appear as setforth in the description above.

The above description is intended to be illustrative, and notrestrictive. Although the present disclosure has been described withreferences to specific illustrative examples, it will be recognized thatthe present disclosure is not limited to the examples described. Thescope of the disclosure should be determined with reference to thefollowing claims, along with the full scope of equivalents to which theclaims are entitled.

What is claimed is:
 1. A computer-implemented method comprising: causingdisplay of a user interface for generating a correlation search, thecorrelation search comprising a search query, a triggering condition tobe applied to a dataset produced by the search query, and one or moreactions to be performed when the dataset produced by the search querysatisfies the triggering condition, wherein the one or more actionscomprise at least modifying a score assigned to an object to which thedataset produced by the search query pertains; receiving, via the userinterface for generating the correlation search, user input identifyingthe one or more actions to be performed when the dataset produced by thesearch query satisfies the triggering condition, the one or more actionscomprising modifying the score assigned to the object; and causinggeneration of the correlation search based on the user input, thecorrelation search reflecting an association between the one or moreactions and the triggering condition.
 2. The computer-implemented methodof claim 1, wherein the user input further indicates one or more searchcriteria for the search query, and the triggering condition to beapplied to the dataset produced by the search query.
 3. Thecomputer-implemented method of claim 1, further comprising: causingdisplay in the user interface of a plurality of statistics types to beused for producing an aggregate on data; receiving, through the userinterface, input identifying a statistics type of the plurality ofstatistics types; receiving input identifying an evaluation of theaggregate produced by the statistics type; and wherein causinggeneration of the correlation search comprises including the evaluationof the aggregate produced by the statistics type in at least one of thesearch query or the triggering condition of the correlation search. 4.The computer-implemented method of claim 1, wherein causing generationof the correlation search comprises: displaying, in the user interface,options for the one or more actions to be performed when the datasetproduced by the search query satisfies the triggering condition;receiving input identifying the one or more actions to be performed; andassociating the one or more actions with the triggering condition. 5.The computer-implemented method of claim 1, wherein the one or moreactions further comprise one or more of updating a display with an entrycorresponding to satisfaction of the triggering condition, or sending anotification indicating satisfaction of the triggering condition.
 6. Thecomputer-implemented method of claim 1, wherein the user interfacecomprises one or more graphical user interface (GUI) elements to receiveinput identifying a time range, the time range defining a scope of datato be searched using the search query.
 7. The computer-implementedmethod of claim 1, wherein the search query corresponds to a searchlanguage that uses a late binding schema.
 8. The computer-implementedmethod of claim 1, further comprising causing execution of the searchquery against raw machine data.
 9. The computer-implemented method ofclaim 1, further comprising causing execution of the search queryagainst time-stamped events that each include a portion of raw machinedata.
 10. The computer-implemented method of claim 1, wherein acalculation of a statistics type is included in the search query. 11.The computer-implemented method of claim 1, wherein an evaluation of acalculation of a statistics type is included in the search query. 12.The computer-implemented method of claim 1, wherein the datasetsatisfies the triggering condition each time the dataset includes anindicator that search criteria of the search query are satisfied. 13.The computer-implemented method of claim 1, wherein the dataset includesa number of times search criteria of the search query are satisfied, andthe dataset satisfies the triggering condition when the number of timesexceeds a threshold.
 14. The computer-implemented method of claim 1,wherein the dataset satisfies the triggering condition when anaggregated statistic pertaining to the dataset exceeds a threshold, isunder a threshold, or is within a specified range.
 15. A systemcomprising: a memory; and a processing device coupled with the memoryto: cause display of a user interface for generating a correlationsearch, the correlation search comprising a search query, a triggeringcondition to be applied to a dataset produced by the search query, andone or more actions to be performed when the dataset produced by thesearch query satisfies the triggering condition, wherein the one or moreactions comprise at least modifying a score assigned to an object towhich the dataset produced by the search query pertains; receive, viathe user interface for generating the correlation search, user inputidentifying the one or more actions to be performed when the datasetproduced by the search query satisfies the triggering condition, the oneor more actions comprising modifying the score assigned to the object;and cause generation of the correlation search based on the user input,the correlation search reflecting an association between the one or moreactions and the triggering condition.
 16. The system of claim 15,wherein the user input further indicates one or more search criteria forthe search query, and the triggering condition to be applied to thedataset produced by the search query.
 17. The system of claim 15,wherein the processing device is further to: cause display in the userinterface of a plurality of statistics types to be used for producing anaggregate on data; receive, through the user interface, inputidentifying a statistics type of the plurality of statistics types;receive input identifying an evaluation of the aggregate produced by thestatistics type; and wherein to cause generation of the correlationsearch, the processing device is further to include the evaluation ofthe aggregate produced by the statistics type in at least one of thesearch query or the triggering condition of the correlation search. 18.A non-transitory computer readable storage medium encoding instructionsthereon that, in response to execution by one or more processingdevices, cause the one or more processing device to perform operationscomprising: causing display of a user interface for generating acorrelation search, the correlation search comprising a search query, atriggering condition to be applied to a dataset produced by the searchquery, and one or more actions to be performed when the dataset producedby the search query satisfies the triggering condition, wherein the oneor more actions comprise at least modifying a score assigned to anobject to which the dataset produced by the search query pertains;receiving, via the user interface for generating the correlation search,user input identifying the one or more actions to be performed when thedataset produced by the search query satisfies the triggering condition,the one or more actions comprising modifying the score assigned to theobject; and causing generation of the correlation search based on theuser input, the correlation search reflecting an association between theone or more actions and the triggering condition.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein causing generationof the correlation search comprises: displaying, in the user interface,options for the one or more actions to be performed when the datasetproduced by the search query satisfies the triggering condition;receiving input identifying the one or more actions to be performed; andassociating the one or more actions with the triggering condition. 20.The non-transitory computer-readable storage medium of claim 18, whereinthe one or more actions further comprise one or more of updating adisplay with an entry corresponding to satisfaction of the triggeringcondition, or sending a notification indicating satisfaction of thetriggering condition.